add memory
This commit is contained in:
191
mcp/chat_client.py
Normal file
191
mcp/chat_client.py
Normal file
@@ -0,0 +1,191 @@
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# chat_client.py
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"""
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Simple Chat Interface for AigptMCP Server
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"""
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import requests
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import json
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import os
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from datetime import datetime
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class AigptChatClient:
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def __init__(self, server_url="http://localhost:5000"):
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self.server_url = server_url
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self.session_id = f"session_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
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self.conversation_history = []
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def send_message(self, message: str) -> str:
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"""メッセージを送信してレスポンスを取得"""
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try:
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# MCPサーバーにメッセージを送信
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response = requests.post(
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f"{self.server_url}/chat",
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json={"message": message},
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headers={"Content-Type": "application/json"}
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)
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if response.status_code == 200:
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data = response.json()
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ai_response = data.get("response", "Sorry, no response received.")
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# 会話履歴を保存
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self.conversation_history.append({
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"role": "user",
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"content": message,
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"timestamp": datetime.now().isoformat()
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})
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self.conversation_history.append({
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"role": "assistant",
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"content": ai_response,
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"timestamp": datetime.now().isoformat()
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})
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# 関係性を更新（簡単な例）
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self.update_relationship(message, ai_response)
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return ai_response
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else:
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return f"Error: {response.status_code} - {response.text}"
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except requests.RequestException as e:
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return f"Connection error: {e}"
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def update_relationship(self, user_message: str, ai_response: str):
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"""関係性を自動更新"""
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try:
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# 簡単な感情分析（実際はもっと高度に）
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positive_words = ["thank", "good", "great", "awesome", "love", "like", "helpful"]
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negative_words = ["bad", "terrible", "hate", "wrong", "stupid", "useless"]
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user_lower = user_message.lower()
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interaction_type = "neutral"
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weight = 1.0
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if any(word in user_lower for word in positive_words):
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interaction_type = "positive"
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weight = 2.0
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elif any(word in user_lower for word in negative_words):
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interaction_type = "negative"
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weight = 2.0
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# 関係性を更新
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requests.post(
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f"{self.server_url}/relationship/update",
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json={
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"target": "user_general",
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"interaction_type": interaction_type,
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"weight": weight,
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"context": f"Chat: {user_message[:50]}..."
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}
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)
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except:
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pass # 関係性更新に失敗しても継続
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def search_memories(self, query: str) -> list:
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"""記憶を検索"""
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try:
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response = requests.post(
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f"{self.server_url}/memory/search",
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json={"query": query, "limit": 5}
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)
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if response.status_code == 200:
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return response.json().get("results", [])
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except:
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pass
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return []
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def get_relationship_status(self) -> dict:
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"""関係性ステータスを取得"""
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try:
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response = requests.get(f"{self.server_url}/relationship/check?target=user_general")
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if response.status_code == 200:
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return response.json()
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except:
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pass
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return {}
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def save_conversation(self):
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"""会話を保存"""
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if not self.conversation_history:
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return
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conversation_data = {
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"session_id": self.session_id,
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"start_time": self.conversation_history[0]["timestamp"],
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"end_time": self.conversation_history[-1]["timestamp"],
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"messages": self.conversation_history,
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"message_count": len(self.conversation_history)
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}
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filename = f"conversation_{self.session_id}.json"
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with open(filename, 'w', encoding='utf-8') as f:
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json.dump(conversation_data, f, ensure_ascii=False, indent=2)
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print(f"💾 Conversation saved to {filename}")
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def main():
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"""メインのチャットループ"""
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print("🤖 AigptMCP Chat Interface")
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print("Type 'quit' to exit, 'save' to save conversation, 'status' for relationship status")
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print("=" * 50)
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client = AigptChatClient()
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# サーバーの状態をチェック
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try:
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response = requests.get(client.server_url)
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if response.status_code == 200:
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print("✅ Connected to AigptMCP Server")
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else:
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print("❌ Failed to connect to server")
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return
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except:
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print("❌ Server not running. Please start with: python mcp/server.py")
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return
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while True:
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try:
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user_input = input("\n👤 You: ").strip()
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if not user_input:
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continue
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if user_input.lower() == 'quit':
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client.save_conversation()
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print("👋 Goodbye!")
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break
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elif user_input.lower() == 'save':
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client.save_conversation()
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continue
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elif user_input.lower() == 'status':
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status = client.get_relationship_status()
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if status:
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print(f"📊 Relationship Score: {status.get('score', 0):.1f}")
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print(f"📤 Can Send Messages: {'Yes' if status.get('can_send_message') else 'No'}")
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else:
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print("❌ Failed to get relationship status")
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continue
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elif user_input.lower().startswith('search '):
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query = user_input[7:] # Remove 'search '
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memories = client.search_memories(query)
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if memories:
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print(f"🔍 Found {len(memories)} related memories:")
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for memory in memories:
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print(f" - {memory['title']}: {memory.get('ai_summary', memory.get('basic_summary', ''))[:100]}...")
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else:
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print("🔍 No related memories found")
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continue
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# 通常のチャット
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print("🤖 AI: ", end="", flush=True)
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response = client.send_message(user_input)
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print(response)
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except KeyboardInterrupt:
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client.save_conversation()
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print("\n👋 Goodbye!")
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break
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except Exception as e:
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print(f"❌ Error: {e}")
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if __name__ == "__main__":
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main()
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@@ -1,549 +0,0 @@
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<!DOCTYPE html>
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<html lang="ja">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>改良版 ChatGPT会話コンバーター</title>
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<style>
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body {
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font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
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max-width: 1200px;
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margin: 0 auto;
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padding: 20px;
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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min-height: 100vh;
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color: #333;
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}
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.container {
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background: white;
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border-radius: 20px;
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padding: 30px;
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box-shadow: 0 20px 40px rgba(0,0,0,0.1);
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backdrop-filter: blur(10px);
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}
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.header {
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text-align: center;
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margin-bottom: 30px;
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padding-bottom: 20px;
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border-bottom: 2px solid #e0e0e0;
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}
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.header h1 {
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color: #2c3e50;
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margin: 0;
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font-size: 2.5em;
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font-weight: 300;
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text-shadow: 2px 2px 4px rgba(0,0,0,0.1);
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}
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.upload-area {
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border: 3px dashed #3498db;
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border-radius: 15px;
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padding: 40px 20px;
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text-align: center;
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background: linear-gradient(45deg, #f8f9ff, #e8f4f8);
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margin-bottom: 30px;
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transition: all 0.3s ease;
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cursor: pointer;
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}
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.upload-area:hover {
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border-color: #2980b9;
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background: linear-gradient(45deg, #f0f8ff, #e0f0f8);
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transform: translateY(-2px);
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}
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.upload-area.dragover {
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border-color: #27ae60;
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background: linear-gradient(45deg, #f0fff0, #e0f8e0);
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}
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.upload-icon {
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font-size: 4em;
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color: #3498db;
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margin-bottom: 20px;
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display: block;
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}
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input[type="file"] {
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display: none;
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}
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.btn {
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background: linear-gradient(135deg, #3498db, #2980b9);
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color: white;
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border: none;
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padding: 12px 30px;
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border-radius: 25px;
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cursor: pointer;
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font-size: 16px;
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transition: all 0.3s ease;
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margin: 10px;
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box-shadow: 0 4px 15px rgba(52, 152, 219, 0.3);
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}
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.btn:hover {
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transform: translateY(-2px);
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box-shadow: 0 8px 25px rgba(52, 152, 219, 0.4);
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}
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.btn:disabled {
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background: #bdc3c7;
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cursor: not-allowed;
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transform: none;
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box-shadow: none;
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}
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.stats {
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
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gap: 20px;
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margin: 30px 0;
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}
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.stat-card {
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background: linear-gradient(135deg, #667eea, #764ba2);
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color: white;
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padding: 20px;
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border-radius: 15px;
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text-align: center;
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box-shadow: 0 10px 30px rgba(102, 126, 234, 0.3);
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}
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.stat-value {
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font-size: 2em;
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font-weight: bold;
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margin-bottom: 5px;
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}
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.stat-label {
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font-size: 0.9em;
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opacity: 0.9;
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}
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.progress-bar {
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width: 100%;
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height: 8px;
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background: #ecf0f1;
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border-radius: 4px;
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overflow: hidden;
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margin: 20px 0;
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}
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.progress-fill {
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height: 100%;
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background: linear-gradient(90deg, #3498db, #27ae60);
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transition: width 0.3s ease;
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width: 0%;
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}
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.log {
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background: #2c3e50;
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color: #ecf0f1;
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padding: 20px;
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border-radius: 10px;
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height: 300px;
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overflow-y: auto;
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font-family: 'Courier New', monospace;
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font-size: 14px;
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margin-top: 20px;
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white-space: pre-wrap;
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}
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.error {
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color: #e74c3c;
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}
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.success {
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color: #27ae60;
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}
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.warning {
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color: #f39c12;
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}
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.info {
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color: #3498db;
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}
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@keyframes pulse {
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0% { opacity: 1; }
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50% { opacity: 0.5; }
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100% { opacity: 1; }
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}
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.processing {
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animation: pulse 2s infinite;
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}
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</style>
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</head>
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<body>
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<div class="container">
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<div class="header">
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<h1>🔧 改良版 ChatGPT会話コンバーター</h1>
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<p>画像・検索・特殊メッセージに対応した堅牢な変換ツール</p>
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</div>
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<div class="upload-area" onclick="document.getElementById('file-input').click()">
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<span class="upload-icon">📁</span>
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<h3>ChatGPT会話ファイルをドロップまたはクリックして選択</h3>
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<p>conversations.json ファイルをアップロード</p>
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<input type="file" id="file-input" accept=".json" />
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</div>
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<div class="stats" id="stats" style="display: none;">
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<div class="stat-card">
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<div class="stat-value" id="total-conversations">0</div>
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<div class="stat-label">総会話数</div>
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</div>
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<div class="stat-card">
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<div class="stat-value" id="processed-conversations">0</div>
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<div class="stat-label">処理済み会話</div>
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</div>
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<div class="stat-card">
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<div class="stat-value" id="success-conversations">0</div>
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<div class="stat-label">変換成功</div>
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</div>
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<div class="stat-card">
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<div class="stat-value" id="failed-conversations">0</div>
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<div class="stat-label">変換失敗</div>
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</div>
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</div>
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<div class="progress-bar" id="progress-container" style="display: none;">
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<div class="progress-fill" id="progress-fill"></div>
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</div>
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<div style="text-align: center;">
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<button class="btn" id="convert-btn" disabled>🔄 変換開始</button>
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<button class="btn" id="download-btn" disabled style="background: linear-gradient(135deg, #27ae60, #2ecc71);">📥 結果をダウンロード</button>
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<button class="btn" id="clear-btn" style="background: linear-gradient(135deg, #e74c3c, #c0392b);">🗑️ クリア</button>
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</div>
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<div class="log" id="log"></div>
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</div>
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<script>
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let originalData = null;
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let convertedResults = [];
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// DOM要素
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const fileInput = document.getElementById('file-input');
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const uploadArea = document.querySelector('.upload-area');
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const convertBtn = document.getElementById('convert-btn');
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const downloadBtn = document.getElementById('download-btn');
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const clearBtn = document.getElementById('clear-btn');
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const logElement = document.getElementById('log');
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const statsElement = document.getElementById('stats');
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const progressContainer = document.getElementById('progress-container');
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const progressFill = document.getElementById('progress-fill');
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// 統計要素
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const totalConversationsEl = document.getElementById('total-conversations');
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const processedConversationsEl = document.getElementById('processed-conversations');
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const successConversationsEl = document.getElementById('success-conversations');
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const failedConversationsEl = document.getElementById('failed-conversations');
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// ドラッグ&ドロップ
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uploadArea.addEventListener('dragover', (e) => {
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e.preventDefault();
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uploadArea.classList.add('dragover');
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});
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uploadArea.addEventListener('dragleave', () => {
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uploadArea.classList.remove('dragover');
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});
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uploadArea.addEventListener('drop', (e) => {
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e.preventDefault();
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uploadArea.classList.remove('dragover');
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const files = e.dataTransfer.files;
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if (files.length > 0) {
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handleFile(files[0]);
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}
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});
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// ファイル選択
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fileInput.addEventListener('change', (e) => {
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if (e.target.files.length > 0) {
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handleFile(e.target.files[0]);
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}
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});
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// ファイル処理
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function handleFile(file) {
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if (!file.name.endsWith('.json')) {
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log('❌ JSONファイルを選択してください', 'error');
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return;
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}
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log(`📁 ファイルを読み込み中: ${file.name}`, 'info');
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const reader = new FileReader();
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reader.onload = (e) => {
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try {
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originalData = JSON.parse(e.target.result);
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log(`✅ ファイル読み込み完了 (${(file.size / 1024 / 1024).toFixed(2)}MB)`, 'success');
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// 統計表示
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const totalCount = Array.isArray(originalData) ? originalData.length : 1;
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totalConversationsEl.textContent = totalCount;
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statsElement.style.display = 'grid';
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convertBtn.disabled = false;
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log('🔄 変換準備完了。「変換開始」ボタンをクリックしてください', 'info');
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} catch (error) {
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log(`❌ JSONファイルの解析に失敗: ${error.message}`, 'error');
|
||||
}
|
||||
};
|
||||
reader.readAsText(file);
|
||||
}
|
||||
|
||||
// ログ出力
|
||||
function log(message, type = 'info') {
|
||||
const timestamp = new Date().toLocaleTimeString();
|
||||
const className = type;
|
||||
logElement.innerHTML += `<span class="${className}">[${timestamp}] ${message}</span>\n`;
|
||||
logElement.scrollTop = logElement.scrollHeight;
|
||||
}
|
||||
|
||||
// メッセージの内容を安全に取得
|
||||
function extractMessageContent(message) {
|
||||
if (!message || !message.content) return '';
|
||||
|
||||
const content = message.content;
|
||||
|
||||
// テキストコンテンツの場合
|
||||
if (content.content_type === 'text' && content.parts) {
|
||||
return content.parts
|
||||
.filter(part => part && typeof part === 'string' && part.trim())
|
||||
.join('\n')
|
||||
.trim();
|
||||
}
|
||||
|
||||
// マルチモーダル（画像付き）コンテンツの場合
|
||||
if (content.content_type === 'multimodal_text' && content.parts) {
|
||||
const textParts = [];
|
||||
for (const part of content.parts) {
|
||||
if (typeof part === 'string' && part.trim()) {
|
||||
textParts.push(part);
|
||||
} else if (part && typeof part === 'object') {
|
||||
// 画像や他のメディアの場合
|
||||
if (part.image_url) {
|
||||
textParts.push('[画像が添付されています]');
|
||||
} else if (part.type === 'text' && part.text) {
|
||||
textParts.push(part.text);
|
||||
}
|
||||
}
|
||||
}
|
||||
return textParts.join('\n').trim();
|
||||
}
|
||||
|
||||
// ユーザープロファイル情報の場合
|
||||
if (content.content_type === 'user_editable_context') {
|
||||
return '[システム設定情報]';
|
||||
}
|
||||
|
||||
// その他の特殊コンテンツ
|
||||
if (content.content_type && content.content_type !== 'text') {
|
||||
return `[${content.content_type}]`;
|
||||
}
|
||||
|
||||
return '';
|
||||
}
|
||||
|
||||
// 会話の線形化（親子関係を辿って順序付け）
|
||||
function linearizeConversation(mapping) {
|
||||
const messages = [];
|
||||
const visited = new Set();
|
||||
|
||||
// ルートノードを見つける
|
||||
const rootNode = Object.values(mapping).find(node => node.parent === null);
|
||||
if (!rootNode) {
|
||||
return messages;
|
||||
}
|
||||
|
||||
// 深度優先探索で会話を辿る
|
||||
function traverse(nodeId) {
|
||||
if (visited.has(nodeId) || !mapping[nodeId]) {
|
||||
return;
|
||||
}
|
||||
|
||||
visited.add(nodeId);
|
||||
const node = mapping[nodeId];
|
||||
|
||||
// メッセージが存在し、有効なコンテンツがある場合のみ追加
|
||||
if (node.message) {
|
||||
const message = node.message;
|
||||
const content = extractMessageContent(message);
|
||||
|
||||
// 以下の条件で有効なメッセージとして扱う
|
||||
const isValid = content &&
|
||||
content.length > 0 &&
|
||||
content !== '[システム設定情報]' &&
|
||||
(!message.metadata?.is_visually_hidden_from_conversation ||
|
||||
(message.author?.role === 'user' || message.author?.role === 'assistant'));
|
||||
|
||||
if (isValid) {
|
||||
messages.push({
|
||||
role: message.author?.role || 'unknown',
|
||||
content: content,
|
||||
timestamp: message.create_time || message.update_time || Date.now() / 1000
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// 子ノードを処理（通常は1つだが、分岐がある場合もある）
|
||||
if (node.children && node.children.length > 0) {
|
||||
// 最初の子ノードのみを辿る（最も新しい応答を優先）
|
||||
traverse(node.children[0]);
|
||||
}
|
||||
}
|
||||
|
||||
traverse(rootNode.id);
|
||||
return messages;
|
||||
}
|
||||
|
||||
// 単一会話の変換
|
||||
function convertSingleConversation(conversation, index) {
|
||||
try {
|
||||
if (!conversation.mapping) {
|
||||
throw new Error('mapping が見つかりません');
|
||||
}
|
||||
|
||||
// 会話を線形化
|
||||
const messages = linearizeConversation(conversation.mapping);
|
||||
|
||||
if (messages.length === 0) {
|
||||
throw new Error('有効なメッセージが見つかりません');
|
||||
}
|
||||
|
||||
// 結果の構築
|
||||
const result = {
|
||||
title: conversation.title || `会話 ${index + 1}`,
|
||||
create_time: conversation.create_time || Date.now() / 1000,
|
||||
update_time: conversation.update_time || Date.now() / 1000,
|
||||
conversation_id: conversation.conversation_id || conversation.id || `conv_${index}`,
|
||||
messages: messages,
|
||||
metadata: {
|
||||
original_message_count: Object.keys(conversation.mapping).length,
|
||||
processed_message_count: messages.length,
|
||||
is_archived: conversation.is_archived || false,
|
||||
model_slug: conversation.default_model_slug || 'unknown'
|
||||
}
|
||||
};
|
||||
|
||||
return { success: true, result, error: null };
|
||||
} catch (error) {
|
||||
return {
|
||||
success: false,
|
||||
result: null,
|
||||
error: error.message,
|
||||
conversation_title: conversation.title || `会話 ${index + 1}`
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
// 変換処理
|
||||
convertBtn.addEventListener('click', async () => {
|
||||
if (!originalData) return;
|
||||
|
||||
convertBtn.disabled = true;
|
||||
downloadBtn.disabled = true;
|
||||
convertedResults = [];
|
||||
|
||||
const conversations = Array.isArray(originalData) ? originalData : [originalData];
|
||||
const total = conversations.length;
|
||||
let processed = 0;
|
||||
let success = 0;
|
||||
let failed = 0;
|
||||
|
||||
log(`🔄 ${total}個の会話の変換を開始します...`, 'info');
|
||||
progressContainer.style.display = 'block';
|
||||
|
||||
for (let i = 0; i < conversations.length; i++) {
|
||||
const conversation = conversations[i];
|
||||
|
||||
log(`[${i + 1}/${total}] "${conversation.title || `会話${i + 1}`}" を処理中...`, 'info');
|
||||
|
||||
const result = convertSingleConversation(conversation, i);
|
||||
|
||||
if (result.success) {
|
||||
convertedResults.push(result.result);
|
||||
success++;
|
||||
log(`✅ [${i + 1}/${total}] 変換成功: ${result.result.messages.length}メッセージ`, 'success');
|
||||
} else {
|
||||
failed++;
|
||||
log(`❌ [${i + 1}/${total}] 変換失敗: ${result.error}`, 'error');
|
||||
}
|
||||
|
||||
processed++;
|
||||
|
||||
// 統計更新
|
||||
processedConversationsEl.textContent = processed;
|
||||
successConversationsEl.textContent = success;
|
||||
failedConversationsEl.textContent = failed;
|
||||
|
||||
// プログレスバー更新
|
||||
const progress = (processed / total) * 100;
|
||||
progressFill.style.width = `${progress}%`;
|
||||
|
||||
// UIを更新するため少し待機
|
||||
await new Promise(resolve => setTimeout(resolve, 1));
|
||||
}
|
||||
|
||||
log(`🎉 変換完了! 成功: ${success}個, 失敗: ${failed}個`, success > 0 ? 'success' : 'warning');
|
||||
|
||||
if (success > 0) {
|
||||
downloadBtn.disabled = false;
|
||||
}
|
||||
convertBtn.disabled = false;
|
||||
});
|
||||
|
||||
// ダウンロード
|
||||
downloadBtn.addEventListener('click', () => {
|
||||
if (convertedResults.length === 0) return;
|
||||
|
||||
const blob = new Blob([JSON.stringify(convertedResults, null, 2)], {
|
||||
type: 'application/json'
|
||||
});
|
||||
|
||||
const url = URL.createObjectURL(blob);
|
||||
const a = document.createElement('a');
|
||||
a.href = url;
|
||||
a.download = `chatgpt_conversations_converted_${new Date().toISOString().split('T')[0]}.json`;
|
||||
document.body.appendChild(a);
|
||||
a.click();
|
||||
document.body.removeChild(a);
|
||||
URL.revokeObjectURL(url);
|
||||
|
||||
log('📥 変換結果をダウンロードしました', 'success');
|
||||
});
|
||||
|
||||
// クリア
|
||||
clearBtn.addEventListener('click', () => {
|
||||
originalData = null;
|
||||
convertedResults = [];
|
||||
logElement.innerHTML = '';
|
||||
statsElement.style.display = 'none';
|
||||
progressContainer.style.display = 'none';
|
||||
progressFill.style.width = '0%';
|
||||
|
||||
// ボタン状態リセット
|
||||
convertBtn.disabled = true;
|
||||
downloadBtn.disabled = true;
|
||||
|
||||
// ファイル入力リセット
|
||||
fileInput.value = '';
|
||||
|
||||
log('🗑️ すべてクリアしました', 'info');
|
||||
});
|
||||
|
||||
// 初期メッセージ
|
||||
log('👋 ChatGPT会話コンバーターへようこそ！', 'info');
|
||||
log('📁 conversations.json ファイルをアップロードしてください', 'info');
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,5 +1,8 @@
|
||||
# rerequirements.txt
|
||||
fastapi>=0.104.0
|
||||
uvicorn[standard]>=0.24.0
|
||||
pydantic>=2.5.0
|
||||
requests>=2.31.0
|
||||
python-multipart>=0.0.6
|
||||
aiohttp
|
||||
asyncio
|
||||
|
||||
525
mcp/server.py
525
mcp/server.py
@@ -1,15 +1,18 @@
|
||||
# mcp/server.py
|
||||
"""
|
||||
Enhanced MCP Server with Memory for aigpt CLI
|
||||
Enhanced MCP Server with AI Memory Processing for aigpt CLI
|
||||
"""
|
||||
import json
|
||||
import os
|
||||
from datetime import datetime
|
||||
import hashlib
|
||||
from datetime import datetime, timedelta
|
||||
from pathlib import Path
|
||||
from typing import List, Dict, Any, Optional
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from pydantic import BaseModel
|
||||
import uvicorn
|
||||
import asyncio
|
||||
import aiohttp
|
||||
|
||||
# データモデル
|
||||
class ChatMessage(BaseModel):
|
||||
@@ -23,22 +26,260 @@ class MemoryQuery(BaseModel):
|
||||
class ConversationImport(BaseModel):
|
||||
conversation_data: Dict[str, Any]
|
||||
|
||||
class MemorySummaryRequest(BaseModel):
|
||||
filepath: str
|
||||
ai_provider: Optional[str] = "openai"
|
||||
|
||||
class RelationshipUpdate(BaseModel):
|
||||
target: str # 対象者/トピック
|
||||
interaction_type: str # "positive", "negative", "neutral"
|
||||
weight: float = 1.0
|
||||
context: Optional[str] = None
|
||||
|
||||
# 設定
|
||||
BASE_DIR = Path.home() / ".config" / "aigpt"
|
||||
MEMORY_DIR = BASE_DIR / "memory"
|
||||
CHATGPT_MEMORY_DIR = MEMORY_DIR / "chatgpt"
|
||||
PROCESSED_MEMORY_DIR = MEMORY_DIR / "processed"
|
||||
RELATIONSHIP_DIR = BASE_DIR / "relationships"
|
||||
|
||||
def init_directories():
|
||||
"""必要なディレクトリを作成"""
|
||||
BASE_DIR.mkdir(parents=True, exist_ok=True)
|
||||
MEMORY_DIR.mkdir(parents=True, exist_ok=True)
|
||||
CHATGPT_MEMORY_DIR.mkdir(parents=True, exist_ok=True)
|
||||
PROCESSED_MEMORY_DIR.mkdir(parents=True, exist_ok=True)
|
||||
RELATIONSHIP_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
class MemoryManager:
|
||||
"""記憶管理クラス"""
|
||||
class AIMemoryProcessor:
|
||||
"""AI記憶処理クラス"""
|
||||
|
||||
def __init__(self):
|
||||
# AI APIの設定（環境変数から取得）
|
||||
self.openai_api_key = os.getenv("OPENAI_API_KEY")
|
||||
self.anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
|
||||
|
||||
async def generate_ai_summary(self, messages: List[Dict[str, Any]], provider: str = "openai") -> Dict[str, Any]:
|
||||
"""AIを使用して会話の高度な要約と分析を生成"""
|
||||
|
||||
# 会話内容を結合
|
||||
conversation_text = ""
|
||||
for msg in messages[-20:]: # 最新20メッセージを使用
|
||||
role = "User" if msg["role"] == "user" else "Assistant"
|
||||
conversation_text += f"{role}: {msg['content'][:500]}\n"
|
||||
|
||||
# プロンプトを構築
|
||||
analysis_prompt = f"""
|
||||
以下の会話を分析し、JSON形式で以下の情報を抽出してください：
|
||||
|
||||
1. main_topics: 主なトピック（最大5個）
|
||||
2. user_intent: ユーザーの意図や目的
|
||||
3. key_insights: 重要な洞察や学び（最大3個）
|
||||
4. relationship_indicators: 関係性を示す要素
|
||||
5. emotional_tone: 感情的なトーン
|
||||
6. action_items: アクションアイテムや次のステップ
|
||||
7. summary: 100文字以内の要約
|
||||
|
||||
会話内容:
|
||||
{conversation_text}
|
||||
|
||||
回答はJSON形式のみで返してください。
|
||||
"""
|
||||
|
||||
try:
|
||||
if provider == "openai" and self.openai_api_key:
|
||||
return await self._call_openai_api(analysis_prompt)
|
||||
elif provider == "anthropic" and self.anthropic_api_key:
|
||||
return await self._call_anthropic_api(analysis_prompt)
|
||||
else:
|
||||
# フォールバック：基本的な分析
|
||||
return self._generate_basic_analysis(messages)
|
||||
except Exception as e:
|
||||
print(f"AI analysis failed: {e}")
|
||||
return self._generate_basic_analysis(messages)
|
||||
|
||||
async def _call_openai_api(self, prompt: str) -> Dict[str, Any]:
|
||||
"""OpenAI APIを呼び出し"""
|
||||
async with aiohttp.ClientSession() as session:
|
||||
headers = {
|
||||
"Authorization": f"Bearer {self.openai_api_key}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
data = {
|
||||
"model": "gpt-4",
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"temperature": 0.3,
|
||||
"max_tokens": 1000
|
||||
}
|
||||
|
||||
async with session.post("https://api.openai.com/v1/chat/completions",
|
||||
headers=headers, json=data) as response:
|
||||
result = await response.json()
|
||||
content = result["choices"][0]["message"]["content"]
|
||||
return json.loads(content)
|
||||
|
||||
async def _call_anthropic_api(self, prompt: str) -> Dict[str, Any]:
|
||||
"""Anthropic APIを呼び出し"""
|
||||
async with aiohttp.ClientSession() as session:
|
||||
headers = {
|
||||
"x-api-key": self.anthropic_api_key,
|
||||
"Content-Type": "application/json",
|
||||
"anthropic-version": "2023-06-01"
|
||||
}
|
||||
data = {
|
||||
"model": "claude-3-sonnet-20240229",
|
||||
"max_tokens": 1000,
|
||||
"messages": [{"role": "user", "content": prompt}]
|
||||
}
|
||||
|
||||
async with session.post("https://api.anthropic.com/v1/messages",
|
||||
headers=headers, json=data) as response:
|
||||
result = await response.json()
|
||||
content = result["content"][0]["text"]
|
||||
return json.loads(content)
|
||||
|
||||
def _generate_basic_analysis(self, messages: List[Dict[str, Any]]) -> Dict[str, Any]:
|
||||
"""基本的な分析（AI APIが利用できない場合のフォールバック）"""
|
||||
user_messages = [msg for msg in messages if msg["role"] == "user"]
|
||||
assistant_messages = [msg for msg in messages if msg["role"] == "assistant"]
|
||||
|
||||
# キーワード抽出（簡易版）
|
||||
all_text = " ".join([msg["content"] for msg in messages])
|
||||
words = all_text.lower().split()
|
||||
word_freq = {}
|
||||
for word in words:
|
||||
if len(word) > 3:
|
||||
word_freq[word] = word_freq.get(word, 0) + 1
|
||||
|
||||
top_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)[:5]
|
||||
|
||||
return {
|
||||
"main_topics": [word[0] for word in top_words],
|
||||
"user_intent": "情報収集・問題解決",
|
||||
"key_insights": ["基本的な会話分析"],
|
||||
"relationship_indicators": {
|
||||
"interaction_count": len(messages),
|
||||
"user_engagement": len(user_messages),
|
||||
"assistant_helpfulness": len(assistant_messages)
|
||||
},
|
||||
"emotional_tone": "neutral",
|
||||
"action_items": [],
|
||||
"summary": f"{len(user_messages)}回のやり取りによる会話"
|
||||
}
|
||||
|
||||
class RelationshipTracker:
|
||||
"""関係性追跡クラス"""
|
||||
|
||||
def __init__(self):
|
||||
init_directories()
|
||||
self.relationship_file = RELATIONSHIP_DIR / "relationships.json"
|
||||
self.relationships = self._load_relationships()
|
||||
|
||||
def _load_relationships(self) -> Dict[str, Any]:
|
||||
"""関係性データを読み込み"""
|
||||
if self.relationship_file.exists():
|
||||
with open(self.relationship_file, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
return {"targets": {}, "last_updated": datetime.now().isoformat()}
|
||||
|
||||
def _save_relationships(self):
|
||||
"""関係性データを保存"""
|
||||
self.relationships["last_updated"] = datetime.now().isoformat()
|
||||
with open(self.relationship_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(self.relationships, f, ensure_ascii=False, indent=2)
|
||||
|
||||
def update_relationship(self, target: str, interaction_type: str, weight: float = 1.0, context: str = None):
|
||||
"""関係性を更新"""
|
||||
if target not in self.relationships["targets"]:
|
||||
self.relationships["targets"][target] = {
|
||||
"score": 0.0,
|
||||
"interactions": [],
|
||||
"created_at": datetime.now().isoformat(),
|
||||
"last_interaction": None
|
||||
}
|
||||
|
||||
# スコア計算
|
||||
score_change = 0.0
|
||||
if interaction_type == "positive":
|
||||
score_change = weight * 1.0
|
||||
elif interaction_type == "negative":
|
||||
score_change = weight * -1.0
|
||||
|
||||
# 時間減衰を適用
|
||||
self._apply_time_decay(target)
|
||||
|
||||
# スコア更新
|
||||
current_score = self.relationships["targets"][target]["score"]
|
||||
new_score = current_score + score_change
|
||||
|
||||
# スコアの範囲制限（-100 to 100）
|
||||
new_score = max(-100, min(100, new_score))
|
||||
|
||||
self.relationships["targets"][target]["score"] = new_score
|
||||
self.relationships["targets"][target]["last_interaction"] = datetime.now().isoformat()
|
||||
|
||||
# インタラクション履歴を追加
|
||||
interaction_record = {
|
||||
"type": interaction_type,
|
||||
"weight": weight,
|
||||
"score_change": score_change,
|
||||
"new_score": new_score,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"context": context
|
||||
}
|
||||
|
||||
self.relationships["targets"][target]["interactions"].append(interaction_record)
|
||||
|
||||
# 履歴は最新100件まで保持
|
||||
if len(self.relationships["targets"][target]["interactions"]) > 100:
|
||||
self.relationships["targets"][target]["interactions"] = \
|
||||
self.relationships["targets"][target]["interactions"][-100:]
|
||||
|
||||
self._save_relationships()
|
||||
return new_score
|
||||
|
||||
def _apply_time_decay(self, target: str):
|
||||
"""時間減衰を適用"""
|
||||
target_data = self.relationships["targets"][target]
|
||||
last_interaction = target_data.get("last_interaction")
|
||||
|
||||
if last_interaction:
|
||||
last_time = datetime.fromisoformat(last_interaction)
|
||||
now = datetime.now()
|
||||
days_passed = (now - last_time).days
|
||||
|
||||
# 7日ごとに5%減衰
|
||||
if days_passed > 0:
|
||||
decay_factor = 0.95 ** (days_passed / 7)
|
||||
target_data["score"] *= decay_factor
|
||||
|
||||
def get_relationship_score(self, target: str) -> float:
|
||||
"""関係性スコアを取得"""
|
||||
if target in self.relationships["targets"]:
|
||||
self._apply_time_decay(target)
|
||||
return self.relationships["targets"][target]["score"]
|
||||
return 0.0
|
||||
|
||||
def should_send_message(self, target: str, threshold: float = 50.0) -> bool:
|
||||
"""メッセージ送信の可否を判定"""
|
||||
score = self.get_relationship_score(target)
|
||||
return score >= threshold
|
||||
|
||||
def get_all_relationships(self) -> Dict[str, Any]:
|
||||
"""すべての関係性を取得"""
|
||||
# 全ターゲットに時間減衰を適用
|
||||
for target in self.relationships["targets"]:
|
||||
self._apply_time_decay(target)
|
||||
|
||||
return self.relationships
|
||||
|
||||
class MemoryManager:
|
||||
"""記憶管理クラス（AI処理機能付き）"""
|
||||
|
||||
def __init__(self):
|
||||
init_directories()
|
||||
self.ai_processor = AIMemoryProcessor()
|
||||
self.relationship_tracker = RelationshipTracker()
|
||||
|
||||
def parse_chatgpt_conversation(self, conversation_data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||||
"""ChatGPTの会話データを解析してメッセージを抽出"""
|
||||
@@ -54,7 +295,6 @@ class MemoryManager:
|
||||
content = message.get("content", {})
|
||||
parts = content.get("parts", [])
|
||||
|
||||
# partsが存在し、最初の要素が文字列で空でない場合のみ
|
||||
if parts and isinstance(parts[0], str) and parts[0].strip():
|
||||
message_nodes.append({
|
||||
"id": node_id,
|
||||
@@ -63,18 +303,6 @@ class MemoryManager:
|
||||
"content": parts[0],
|
||||
"parent": node.get("parent")
|
||||
})
|
||||
else:
|
||||
print(f"⚠️ Skipped non-text or empty message in node {node_id}")
|
||||
#if message and message.get("content", {}).get("parts"):
|
||||
# parts = message["content"]["parts"]
|
||||
# if parts and parts[0].strip(): # 空でないメッセージのみ
|
||||
# message_nodes.append({
|
||||
# "id": node_id,
|
||||
# "create_time": message.get("create_time", 0),
|
||||
# "author_role": message["author"]["role"],
|
||||
# "content": parts[0],
|
||||
# "parent": node.get("parent")
|
||||
# })
|
||||
|
||||
# 作成時間でソート
|
||||
message_nodes.sort(key=lambda x: x["create_time"] or 0)
|
||||
@@ -90,8 +318,8 @@ class MemoryManager:
|
||||
|
||||
return messages
|
||||
|
||||
def save_chatgpt_memory(self, conversation_data: Dict[str, Any]) -> str:
|
||||
"""ChatGPTの会話を記憶として保存"""
|
||||
async def save_chatgpt_memory(self, conversation_data: Dict[str, Any], process_with_ai: bool = True) -> str:
|
||||
"""ChatGPTの会話を記憶として保存（AI処理オプション付き）"""
|
||||
title = conversation_data.get("title", "untitled")
|
||||
create_time = conversation_data.get("create_time", datetime.now().timestamp())
|
||||
|
||||
@@ -101,6 +329,17 @@ class MemoryManager:
|
||||
if not messages:
|
||||
raise ValueError("No valid messages found in conversation")
|
||||
|
||||
# AI分析を実行
|
||||
ai_analysis = None
|
||||
if process_with_ai:
|
||||
try:
|
||||
ai_analysis = await self.ai_processor.generate_ai_summary(messages)
|
||||
except Exception as e:
|
||||
print(f"AI analysis failed: {e}")
|
||||
|
||||
# 基本要約を生成
|
||||
basic_summary = self.generate_basic_summary(messages)
|
||||
|
||||
# 保存データを作成
|
||||
memory_data = {
|
||||
"title": title,
|
||||
@@ -108,10 +347,24 @@ class MemoryManager:
|
||||
"import_time": datetime.now().isoformat(),
|
||||
"original_create_time": create_time,
|
||||
"messages": messages,
|
||||
"summary": self.generate_summary(messages)
|
||||
"basic_summary": basic_summary,
|
||||
"ai_analysis": ai_analysis,
|
||||
"message_count": len(messages),
|
||||
"hash": self._generate_content_hash(messages)
|
||||
}
|
||||
|
||||
# ファイル名を生成（タイトルをサニタイズ）
|
||||
# 関係性データを更新
|
||||
if ai_analysis and "relationship_indicators" in ai_analysis:
|
||||
interaction_count = ai_analysis["relationship_indicators"].get("interaction_count", 0)
|
||||
if interaction_count > 10: # 長い会話は関係性にプラス
|
||||
self.relationship_tracker.update_relationship(
|
||||
target="user_general",
|
||||
interaction_type="positive",
|
||||
weight=min(interaction_count / 10, 5.0),
|
||||
context=f"Long conversation: {title}"
|
||||
)
|
||||
|
||||
# ファイル名を生成
|
||||
safe_title = "".join(c for c in title if c.isalnum() or c in (' ', '-', '_')).rstrip()
|
||||
timestamp = datetime.fromtimestamp(create_time).strftime("%Y%m%d_%H%M%S")
|
||||
filename = f"{timestamp}_{safe_title[:50]}.json"
|
||||
@@ -120,14 +373,19 @@ class MemoryManager:
|
||||
with open(filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(memory_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 処理済みメモリディレクトリにも保存
|
||||
if ai_analysis:
|
||||
processed_filepath = PROCESSED_MEMORY_DIR / filename
|
||||
with open(processed_filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(memory_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
return str(filepath)
|
||||
|
||||
def generate_summary(self, messages: List[Dict[str, Any]]) -> str:
|
||||
"""会話の要約を生成"""
|
||||
def generate_basic_summary(self, messages: List[Dict[str, Any]]) -> str:
|
||||
"""基本要約を生成"""
|
||||
if not messages:
|
||||
return "Empty conversation"
|
||||
|
||||
# 簡単な要約を生成（実際のAIによる要約は後で実装可能）
|
||||
user_messages = [msg for msg in messages if msg["role"] == "user"]
|
||||
assistant_messages = [msg for msg in messages if msg["role"] == "assistant"]
|
||||
|
||||
@@ -139,37 +397,64 @@ class MemoryManager:
|
||||
|
||||
return summary
|
||||
|
||||
def search_memories(self, query: str, limit: int = 10) -> List[Dict[str, Any]]:
|
||||
"""記憶を検索"""
|
||||
def _generate_content_hash(self, messages: List[Dict[str, Any]]) -> str:
|
||||
"""メッセージ内容のハッシュを生成"""
|
||||
content = "".join([msg["content"] for msg in messages])
|
||||
return hashlib.sha256(content.encode()).hexdigest()[:16]
|
||||
|
||||
def search_memories(self, query: str, limit: int = 10, use_ai_analysis: bool = True) -> List[Dict[str, Any]]:
|
||||
"""記憶を検索（AI分析結果も含む）"""
|
||||
results = []
|
||||
|
||||
# ChatGPTの記憶を検索
|
||||
for filepath in CHATGPT_MEMORY_DIR.glob("*.json"):
|
||||
try:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
memory_data = json.load(f)
|
||||
|
||||
# 簡単なキーワード検索
|
||||
search_text = f"{memory_data.get('title', '')} {memory_data.get('summary', '')}"
|
||||
for msg in memory_data.get('messages', []):
|
||||
search_text += f" {msg.get('content', '')}"
|
||||
|
||||
if query.lower() in search_text.lower():
|
||||
results.append({
|
||||
"filepath": str(filepath),
|
||||
"title": memory_data.get("title"),
|
||||
"summary": memory_data.get("summary"),
|
||||
"source": memory_data.get("source"),
|
||||
"import_time": memory_data.get("import_time"),
|
||||
"message_count": len(memory_data.get("messages", []))
|
||||
})
|
||||
# 処理済みメモリから検索
|
||||
search_dirs = [PROCESSED_MEMORY_DIR, CHATGPT_MEMORY_DIR] if use_ai_analysis else [CHATGPT_MEMORY_DIR]
|
||||
|
||||
for search_dir in search_dirs:
|
||||
for filepath in search_dir.glob("*.json"):
|
||||
try:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
memory_data = json.load(f)
|
||||
|
||||
if len(results) >= limit:
|
||||
break
|
||||
# 検索対象テキストを構築
|
||||
search_text = f"{memory_data.get('title', '')} {memory_data.get('basic_summary', '')}"
|
||||
|
||||
# AI分析結果も検索対象に含める
|
||||
if memory_data.get('ai_analysis'):
|
||||
ai_analysis = memory_data['ai_analysis']
|
||||
search_text += f" {' '.join(ai_analysis.get('main_topics', []))}"
|
||||
search_text += f" {ai_analysis.get('summary', '')}"
|
||||
search_text += f" {' '.join(ai_analysis.get('key_insights', []))}"
|
||||
|
||||
# メッセージ内容も検索対象に含める
|
||||
for msg in memory_data.get('messages', []):
|
||||
search_text += f" {msg.get('content', '')}"
|
||||
|
||||
if query.lower() in search_text.lower():
|
||||
result = {
|
||||
"filepath": str(filepath),
|
||||
"title": memory_data.get("title"),
|
||||
"basic_summary": memory_data.get("basic_summary"),
|
||||
"source": memory_data.get("source"),
|
||||
"import_time": memory_data.get("import_time"),
|
||||
"message_count": len(memory_data.get("messages", [])),
|
||||
"has_ai_analysis": bool(memory_data.get("ai_analysis"))
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error reading memory file {filepath}: {e}")
|
||||
continue
|
||||
if memory_data.get('ai_analysis'):
|
||||
result["ai_summary"] = memory_data['ai_analysis'].get('summary', '')
|
||||
result["main_topics"] = memory_data['ai_analysis'].get('main_topics', [])
|
||||
|
||||
results.append(result)
|
||||
|
||||
if len(results) >= limit:
|
||||
break
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error reading memory file {filepath}: {e}")
|
||||
continue
|
||||
|
||||
if len(results) >= limit:
|
||||
break
|
||||
|
||||
return results
|
||||
|
||||
@@ -190,14 +475,21 @@ class MemoryManager:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
memory_data = json.load(f)
|
||||
|
||||
memories.append({
|
||||
memory_info = {
|
||||
"filepath": str(filepath),
|
||||
"title": memory_data.get("title"),
|
||||
"summary": memory_data.get("summary"),
|
||||
"basic_summary": memory_data.get("basic_summary"),
|
||||
"source": memory_data.get("source"),
|
||||
"import_time": memory_data.get("import_time"),
|
||||
"message_count": len(memory_data.get("messages", []))
|
||||
})
|
||||
"message_count": len(memory_data.get("messages", [])),
|
||||
"has_ai_analysis": bool(memory_data.get("ai_analysis"))
|
||||
}
|
||||
|
||||
if memory_data.get('ai_analysis'):
|
||||
memory_info["ai_summary"] = memory_data['ai_analysis'].get('summary', '')
|
||||
memory_info["main_topics"] = memory_data['ai_analysis'].get('main_topics', [])
|
||||
|
||||
memories.append(memory_info)
|
||||
except Exception as e:
|
||||
print(f"Error reading memory file {filepath}: {e}")
|
||||
continue
|
||||
@@ -207,22 +499,57 @@ class MemoryManager:
|
||||
return memories
|
||||
|
||||
# FastAPI アプリケーション
|
||||
app = FastAPI(title="AigptMCP Server with Memory", version="1.0.0")
|
||||
app = FastAPI(title="AigptMCP Server with AI Memory", version="2.0.0")
|
||||
memory_manager = MemoryManager()
|
||||
|
||||
@app.post("/memory/import/chatgpt")
|
||||
async def import_chatgpt_conversation(data: ConversationImport):
|
||||
"""ChatGPTの会話をインポート"""
|
||||
async def import_chatgpt_conversation(data: ConversationImport, process_with_ai: bool = True):
|
||||
"""ChatGPTの会話をインポート（AI処理オプション付き）"""
|
||||
try:
|
||||
filepath = memory_manager.save_chatgpt_memory(data.conversation_data)
|
||||
filepath = await memory_manager.save_chatgpt_memory(data.conversation_data, process_with_ai)
|
||||
return {
|
||||
"success": True,
|
||||
"message": "Conversation imported successfully",
|
||||
"filepath": filepath
|
||||
"filepath": filepath,
|
||||
"ai_processed": process_with_ai
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
|
||||
@app.post("/memory/process-ai")
|
||||
async def process_memory_with_ai(data: MemorySummaryRequest):
|
||||
"""既存の記憶をAIで再処理"""
|
||||
try:
|
||||
# 既存記憶を読み込み
|
||||
memory_data = memory_manager.get_memory_detail(data.filepath)
|
||||
|
||||
# AI分析を実行
|
||||
ai_analysis = await memory_manager.ai_processor.generate_ai_summary(
|
||||
memory_data["messages"],
|
||||
data.ai_provider
|
||||
)
|
||||
|
||||
# データを更新
|
||||
memory_data["ai_analysis"] = ai_analysis
|
||||
memory_data["ai_processed_at"] = datetime.now().isoformat()
|
||||
|
||||
# ファイルを更新
|
||||
with open(data.filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(memory_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 処理済みディレクトリにもコピー
|
||||
processed_filepath = PROCESSED_MEMORY_DIR / Path(data.filepath).name
|
||||
with open(processed_filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(memory_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"message": "Memory processed with AI successfully",
|
||||
"ai_analysis": ai_analysis
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.post("/memory/search")
|
||||
async def search_memories(query: MemoryQuery):
|
||||
"""記憶を検索"""
|
||||
@@ -261,9 +588,52 @@ async def get_memory_detail(filepath: str):
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
|
||||
@app.post("/relationship/update")
|
||||
async def update_relationship(data: RelationshipUpdate):
|
||||
"""関係性を更新"""
|
||||
try:
|
||||
new_score = memory_manager.relationship_tracker.update_relationship(
|
||||
data.target, data.interaction_type, data.weight, data.context
|
||||
)
|
||||
return {
|
||||
"success": True,
|
||||
"new_score": new_score,
|
||||
"can_send_message": memory_manager.relationship_tracker.should_send_message(data.target)
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/relationship/list")
|
||||
async def list_relationships():
|
||||
"""すべての関係性をリスト"""
|
||||
try:
|
||||
relationships = memory_manager.relationship_tracker.get_all_relationships()
|
||||
return {
|
||||
"success": True,
|
||||
"relationships": relationships
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get("/relationship/check")
|
||||
async def check_send_permission(target: str, threshold: float = 50.0):
|
||||
"""メッセージ送信可否をチェック"""
|
||||
try:
|
||||
score = memory_manager.relationship_tracker.get_relationship_score(target)
|
||||
can_send = memory_manager.relationship_tracker.should_send_message(target, threshold)
|
||||
return {
|
||||
"success": True,
|
||||
"target": target,
|
||||
"score": score,
|
||||
"can_send_message": can_send,
|
||||
"threshold": threshold
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.post("/chat")
|
||||
async def chat_endpoint(data: ChatMessage):
|
||||
"""チャット機能（記憶を活用）"""
|
||||
"""チャット機能（記憶と関係性を活用）"""
|
||||
try:
|
||||
# 関連する記憶を検索
|
||||
memories = memory_manager.search_memories(data.message, limit=3)
|
||||
@@ -273,9 +643,14 @@ async def chat_endpoint(data: ChatMessage):
|
||||
if memories:
|
||||
memory_context = "\n# Related memories:\n"
|
||||
for memory in memories:
|
||||
memory_context += f"- {memory['title']}: {memory['summary']}\n"
|
||||
memory_context += f"- {memory['title']}: {memory.get('ai_summary', memory.get('basic_summary', ''))}\n"
|
||||
if memory.get('main_topics'):
|
||||
memory_context += f" Topics: {', '.join(memory['main_topics'])}\n"
|
||||
|
||||
# 実際のチャット処理（他のプロバイダーに転送）
|
||||
# 関係性情報を取得
|
||||
relationships = memory_manager.relationship_tracker.get_all_relationships()
|
||||
|
||||
# 実際のチャット処理
|
||||
enhanced_message = data.message
|
||||
if memory_context:
|
||||
enhanced_message = f"{data.message}\n\n{memory_context}"
|
||||
@@ -283,7 +658,12 @@ async def chat_endpoint(data: ChatMessage):
|
||||
return {
|
||||
"success": True,
|
||||
"response": f"Enhanced response with memory context: {enhanced_message}",
|
||||
"memories_used": len(memories)
|
||||
"memories_used": len(memories),
|
||||
"relationship_info": {
|
||||
"active_relationships": len(relationships.get("targets", {})),
|
||||
"can_initiate_conversations": sum(1 for target, data in relationships.get("targets", {}).items()
|
||||
if memory_manager.relationship_tracker.should_send_message(target))
|
||||
}
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
@@ -292,19 +672,32 @@ async def chat_endpoint(data: ChatMessage):
|
||||
async def root():
|
||||
"""ヘルスチェック"""
|
||||
return {
|
||||
"service": "AigptMCP Server with Memory",
|
||||
"service": "AigptMCP Server with AI Memory",
|
||||
"version": "2.0.0",
|
||||
"status": "running",
|
||||
"memory_dir": str(MEMORY_DIR),
|
||||
"features": [
|
||||
"AI-powered memory analysis",
|
||||
"Relationship tracking",
|
||||
"Advanced memory search",
|
||||
"Conversation import",
|
||||
"Auto-summary generation"
|
||||
],
|
||||
"endpoints": [
|
||||
"/memory/import/chatgpt",
|
||||
"/memory/process-ai",
|
||||
"/memory/search",
|
||||
"/memory/list",
|
||||
"/memory/detail",
|
||||
"/relationship/update",
|
||||
"/relationship/list",
|
||||
"/relationship/check",
|
||||
"/chat"
|
||||
]
|
||||
}
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("🚀 AigptMCP Server with Memory starting...")
|
||||
print("🚀 AigptMCP Server with AI Memory starting...")
|
||||
print(f"📁 Memory directory: {MEMORY_DIR}")
|
||||
print(f"🧠 AI Memory processing: {'✅ Enabled' if os.getenv('OPENAI_API_KEY') or os.getenv('ANTHROPIC_API_KEY') else '❌ Disabled (no API keys)'}")
|
||||
uvicorn.run(app, host="127.0.0.1", port=5000)
|
||||
|
||||
Reference in New Issue
Block a user